Monthly Archives: September 2014

10.01.14

This entry was posted in Abstracts and tagged Algorithms biological networks breadth depth Eigenvector Centrality social networks Systems Biology on September 30, 2014 by ppointer

A Family of Algorithms for Computing Consensus about Node State from Network Data

Eleanor R. Brush, David C. Krakauer, Jessica C. Flack

Abstract

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node’s state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node’s direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms’ cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.

09.17.14

This entry was posted in Abstracts discussion and tagged DNA methlation epigenetics gene expression gioblastoma PARADIGM Systems Biology on September 15, 2014 by ppointer

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

Charles J. Vaske1,†, Stephen C. Benz2,†, J. Zachary Sanborn2, Dent Earl2, Christopher Szeto2, Jingchun Zhu2, David Haussler1,2 and Joshua M. Stuart2,*

+ Author Affiliations

1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA

* To whom correspondence should be addressed.

Abstract

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.

Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.

Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway’s activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference.

Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.

Availability:Source code available at http://sbenz.github.com/Paradigm

Contact: jstuart@soe.ucsc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

09.03.14

This entry was posted in Abstracts and tagged Systems Biology TP53 TP63 within-a-tissue on September 2, 2014 by ppointer

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Katherine A. Hoadley¹^,²⁰, Christina Yau²^,²⁰, Denise M. Wolf³^,²⁰, Andrew D. Cherniack⁴^,²⁰, David Tamborero⁵, Sam Ng⁶, Max D.M. Leiserson⁷, Beifang Niu⁸, Michael D. McLellan⁸, Vladislav Uzunangelov⁶, Jiashan Zhang⁹, Cyriac Kandoth⁸, Rehan Akbani¹⁰, Hui Shen¹¹^,²², Larsson Omberg¹², Andy Chu¹³, Adam A. Margolin¹²^,²¹, Laura J. van’t Veer³, Nuria Lopez-Bigas⁵^,¹⁴, Peter W. Laird¹¹^,²², Benjamin J. Raphael⁷, Li Ding⁸, A. Gordon Robertson¹³, Lauren A. Byers¹⁰, Gordon B. Mills¹⁰, John N. Weinstein¹⁰, Carter Van Waes¹⁸, Zhong Chen¹⁹, Eric A. Collisson¹⁵,The Cancer Genome Atlas Research Network, Christopher C. Benz²^,^,, Charles M. Perou¹^,¹⁶^,¹⁷^,^,, Joshua M. Stuart⁶^,^,

¹ Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
² Buck Institute for Research on Aging, Novato, CA 94945, USA
³ Department of Laboratory Medicine, University of California San Francisco, 2340 Sutter St, San Francisco, CA, 94115, USA
⁴ The Eli and Edythe Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
⁵ Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona 08003, Spain
⁶ Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA
⁷ Department of Computer Science and Center for Computational Molecular Biology, Brown University, 115 Waterman St, Providence RI 02912, USA
⁸ The Genome Institute, Washington University, St Louis, MO 63108, USA
⁹ National Cancer Institute, NIH, Bethesda, MD 20892, USA
¹⁰ UT MD Anderson Cancer Center, Bioinformatics and Computational Biology, 1400 Pressler Street, Unit 1410, Houston, TX 77030, USA
¹¹ USC Epigenome Center, University of Southern California Keck School of Medicine, 1450 Biggy Street, Los Angeles, CA 90033, USA
¹² Sage Bionetworks 1100 Fairview Avenue North, M1-C108, Seattle, WA 98109-1024, USA
¹³ Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
¹⁴ Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys, 23, Barcelona 08010, Spain
¹⁵ Department of Medicine, University of California San Francisco, 450 35d St, San Francisco, CA, 94148, USA
¹⁶ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
¹⁷ Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
¹⁸ Building 10, Room 4-2732, NIDCD/NIH, 10 Center Drive, Bethesda, MD 20892
¹⁹ Head and Neck Surgery Branch, NIDCD/NIH, 10 Center Drive, Room 5D55, Bethesda, MD 20892

Received 23 December 2013, Revised 4 April 2014, Accepted 23 June 2014, Available online 7 August 2014

Summary

Recent genomic analyses of pathologically defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.